/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    GaussianPrior.java
 *    Copyright (C) 2008 Illinois Institute of Technology
 *
 */
package weka.classifiers.bayes.blr;

import weka.classifiers.bayes.BayesianLogisticRegression;

import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;

/**
 * Implementation of the Gaussian Prior update function based on CLG Algorithm
 * with a certain Trust Region Update.
 * 
 * The values are updated in the BayesianLogisticRegressionV variables used by
 * the algorithm.
 * 
 * 
 * @author Navendu Garg(gargnav@iit.edu)
 * @version $Revision: 1.2 $
 */
public class GaussianPriorImpl extends Prior {

	/** for serialization. */
	private static final long serialVersionUID = -2995684220141159223L;

	/**
	 * Update function specific to Laplace Prior.
	 */
	public double update(int j, Instances instances, double beta,
			double hyperparameter, double[] r, double deltaV) {
		int i;
		double numerator = 0.0;
		double denominator = 0.0;
		double value = 0.0;
		Instance instance;

		m_Instances = instances;
		Beta = beta;
		Hyperparameter = hyperparameter;
		Delta = deltaV;
		R = r;

		// Compute First Derivative i.e. Numerator
		// Compute the Second Derivative i.e.
		for (i = 0; i < m_Instances.numInstances(); i++) {
			instance = m_Instances.instance(i);

			if (instance.value(j) != 0) {
				// Compute Numerator (Note: (0.0-1.0/(1.0+Math.exp(R[i])
				numerator += ((instance.value(j) * BayesianLogisticRegression
						.classSgn(instance.classValue())) * (0.0 - (1.0 / (1.0 + Math
						.exp(R[i])))));

				// Compute Denominator
				denominator += (instance.value(j) * instance.value(j) * BayesianLogisticRegression
						.bigF(R[i], Delta * Math.abs(instance.value(j))));
			}
		}

		numerator += ((2.0 * Beta) / Hyperparameter);
		denominator += (2.0 / Hyperparameter);
		value = numerator / denominator;

		return (0 - (value));
	}

	/**
	 * This method calls the log-likelihood implemented in the Prior abstract
	 * class.
	 * 
	 * @param betas
	 * @param instances
	 */
	public void computeLoglikelihood(double[] betas, Instances instances) {
		super.computelogLikelihood(betas, instances);
	}

	/**
	 * This function computes the penalty term specific to Gaussian
	 * distribution.
	 * 
	 * @param betas
	 * @param hyperparameters
	 */
	public void computePenalty(double[] betas, double[] hyperparameters) {
		penalty = 0.0;

		for (int j = 0; j < betas.length; j++) {
			penalty += (Math.log(Math.sqrt(hyperparameters[j]))
					+ (Math.log(2 * Math.PI) / 2) + ((betas[j] * betas[j]) / (2 * hyperparameters[j])));
		}

		penalty = 0 - penalty;
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 1.2 $");
	}
}
